Reinforcement Learning. For X-MAGICAL, we follow [1]} and learn a state based policy. The state vector has dimensions of 16 and 17 for the Standard and Diverse environments respectively. The Diverse environment state has an additional dimension to represent the size of blocks. For xArm, we learn an image based policy. Specifically, we use first-person and third-person cameras to learn a policy from multi-view image data. We extract \(84 \times 84\)  image from both cameras and concatenate them channel-wise. We use the network architecture and attention mechanism proposed in [2]}. Additionally, we apply data augmentation techniques: random \(\pm 4\)  pixel shift [3]} and color jitter [4]}.
